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How a multi-head CNN with position embeddings achieved 100% accuracy on fixed-length CAPTCHA OCR without using CRNNs or CTC loss.
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This article documents the design of a lightweight OCR system built to solve fixed-length numeric CAPTCHAs for authorized internal automation workflows. Instead of using a standard CRNN + CTC architecture, the author built a shared CNN backbone with six independent classification heads and learnable position embeddings, achieving 100% held-out accuracy with roughly 4,000 training samples while improving training stability, inference speed, and debuggability